Spatial Interpolation


Spatial interpolation is a method used in Geographic Information Systems (GIS) that estimates the values of data points at an un-sampled site within an area, based on sampled points from around that area. Spatial interpolation assumes that the things that are close to one another are more alike than those that are farther apart. This concept is known as "Tobler's First Law of Geography" and is the primary principle underlying the concept of spatial interpolation.

What is Spatial Interpolation?

Spatial interpolation is used to predict values for cells in a raster from a limited number of sample data points. It can be linear or non-linear and depends on the method employed. The goal of spatial interpolation is to provide a complete surface derived from a limited set of sample points.

There are various methods used in spatial interpolation, including:

  1. Nearest Neighbor: This method uses the value of the nearest point and assumes it for the unknown areas.
  2. Inverse Distance Weighting (IDW): It assumes that the points that are closer have more resemblance to each other than to those further apart.
  3. Kriging: This method forms an estimator that is both linear and unbiased, allowing control over the smoothness of the resulting surface.
  4. Spline: It involves the fitting of a mathematical function that minimizes overall surface curvature, resulting in the smooth surface that runs exactly through the input points.

Spatial interpolation plays a crucial role in geostatistics, meteorology, environmental science, and various other fields where geographical data are collected and analyzed.


What is the need for Spatial Interpolation?

Spatial interpolation is necessary when you have a set of data points on a geographical plane and want to predict the values for the entire area. It eliminates data gaps and allows for more comprehensive spatial analysis.

What is the difference between Kriging and Inverse Distance Weighting (IDW)?

The primary difference between Kriging and IDW is that Kriging uses both the distance and the degree of variation between points to derive an estimate, whereas IDW only uses the distance information.

What are the limitations of Spatial Interpolation?

Spatial interpolation has a few limitations. It is based on the assumption that spatial patterns are homogeneous across the study area, which may not always be the case. It also assumes what the trends are influenced by the nearby points and that the characteristics of the dataset are identical in all directions.

Where is Spatial Interpolation Used?

Spatial interpolation is widely used in environmental studies, agriculture, climatology, geology, and any other field requiring detailed geospatial data.

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